Artificial intelligence apparatus

ABSTRACT

Disclosed herein is an artificial intelligence apparatus including an input interface configured to receive speech data, and a processor configured to detect a non-utterance interval included in the speech data and determine presence/absence of a second utterance after the non-utterance interval according to characteristics of a first utterance before the non-utterance interval, when the non-utterance interval exceeds a set time.

CROSS-REFERENCE TO RELATED APPLICATIONS

Pursuant to 35 U.S.C. § 119(a), this application claims the benefit ofearlier filing date and right of priority to Korean Patent ApplicationNo. 10-2019-0141677, filed on Nov. 7, 2019, the contents of which arehereby incorporated by reference herein in its entirety.

BACKGROUND

The present disclosure relates to an artificial intelligence (AI)apparatus capable of detecting a non-utterance interval between a firstutterance and a second utterance and receiving the second utterancewhile a speech command according to the first utterance is performed,when the first utterance is received and the second utterance isadditionally received.

Recently, speech recognition technology has been applied to variousfields. The speech recognition technology may refer to a process ofperforming conversion such that an artificial intelligence apparatusunderstands speech data spoken by a user, and a speech recognitionservice using speech recognition technology may include recognizing thespeech of a user and providing a suitable service corresponding thereto.

Currently, when a user speaks toward an apparatus having a speechrecognition function, the speech recognition function of the apparatusis activated via a wakeup word. When the speech recognition function isactivated, the apparatus may recognize a speech according to a utteranceand perform a command corresponding thereto. After the command accordingto speech recognition is performed, the user may speak a wakeup wordagain to activate the speech recognition function, and the apparatus mayperform a speech command according to the new utterance.

As such, in order for an apparatus having a speech recognition functionto perform various operations according to speech recognition, it isnecessary to receive a wakeup word several times.

SUMMARY

An object of the present disclosure is to provide an artificialintelligence apparatus capable of receiving speech data, detecting anon-utterance interval, and additionally receiving a second utterancewhile operation according to a first utterance is performed.

An artificial intelligence apparatus according to the present disclosuremay receive speech data, detect a non-utterance interval included in thespeech data, and determine presence/absence of a second utterance afterthe non-utterance interval according to characteristics of a firstutterance before the non-utterance interval, when the non-utteranceinterval exceeds a set time.

When the second utterance is present, the artificial intelligenceapparatus may perform a first speech command according to the firstutterance while the second utterance is received. In addition, when thesecond utterance is received, a second speech command according to thesecond utterance may be performed.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram illustrating an AI apparatus according to anembodiment of the present disclosure.

FIG. 2 is a block diagram illustrating an AI server according to anembodiment of the present disclosure.

FIG. 3 is a view illustrating an AI system according to an embodiment ofthe present disclosure.

FIG. 4 is a block diagram illustrating an AI apparatus according to anembodiment of the present disclosure.

FIG. 5 is a flowchart illustrating an embodiment of the presentdisclosure.

FIG. 6 is a flowchart illustrating an embodiment of the presentdisclosure.

FIG. 7 is a view illustrating an artificial intelligence model accordingto an embodiment of the present disclosure.

FIG. 8 is a view illustrating an example of utterance according to anembodiment of the present disclosure.

FIG. 9 is a view illustrating a scenario according to an embodiment ofthe present disclosure.

DETAILED DESCRIPTION OF THE EMBODIMENTS

Hereinafter, embodiments of the present disclosure are described in moredetail with reference to accompanying drawings and regardless of thedrawings symbols, same or similar components are assigned with the samereference numerals and thus overlapping descriptions for those areomitted. The suffixes “module” and “interface” for components used inthe description below are assigned or mixed in consideration of easinessin writing the specification and do not have distinctive meanings orroles by themselves. In the following description, detailed descriptionsof well-known functions or constructions will be omitted since theywould obscure the disclosure in unnecessary detail. Additionally, theaccompanying drawings are used to help easily understanding embodimentsdisclosed herein but the technical idea of the present disclosure is notlimited thereto. It should be understood that all of variations,equivalents or substitutes contained in the concept and technical scopeof the present disclosure are also included.

It will be understood that the terms “first” and “second” are usedherein to describe various components but these components should not belimited by these terms. These terms are used only to distinguish onecomponent from other components.

In this disclosure below, when one part (or element, device, etc.) isreferred to as being ‘connected’ to another part (or element, device,etc.), it should be understood that the former can be ‘directlyconnected’ to the latter, or ‘electrically connected’ to the latter viaan intervening part (or element, device, etc.). It will be furtherunderstood that when one component is referred to as being ‘directlyconnected’ or ‘directly linked’ to another component, it means that nointervening component is present.

<Artificial Intelligence (AI)>

Artificial intelligence refers to the field of studying artificialintelligence or methodology for making artificial intelligence, andmachine learning refers to the field of defining various issues dealtwith in the field of artificial intelligence and studying methodologyfor solving the various issues. Machine learning is defined as analgorithm that enhances the performance of a certain task through asteady experience with the certain task.

An artificial neural network (ANN) is a model used in machine learningand may mean a whole model of problem-solving ability which is composedof artificial neurons (nodes) that form a network by synapticconnections. The artificial neural network can be defined by aconnection pattern between neurons in different layers, a learningprocess for updating model parameters, and an activation function forgenerating an output value.

The artificial neural network may include an input layer, an outputlayer, and optionally one or more hidden layers. Each layer includes oneor more neurons, and the artificial neural network may include a synapsethat links neurons to neurons. In the artificial neural network, eachneuron may output the function value of the activation function forinput signals, weights, and deflections input through the synapse.

Model parameters refer to parameters determined through learning andinclude a weight value of synaptic connection and deflection of neurons.A hyperparameter means a parameter to be set in the machine learningalgorithm before learning, and includes a learning rate, a repetitionnumber, a mini batch size, and an initialization function.

The purpose of the learning of the artificial neural network may be todetermine the model parameters that minimize a loss function. The lossfunction may be used as an index to determine optimal model parametersin the learning process of the artificial neural network.

Machine learning may be classified into supervised learning,unsupervised learning, and reinforcement learning according to alearning method.

The supervised learning may refer to a method of learning an artificialneural network in a state in which a label for training data is given,and the label may mean the correct answer (or result value) that theartificial neural network must infer when the training data is input tothe artificial neural network. The unsupervised learning may refer to amethod of learning an artificial neural network in a state in which alabel for training data is not given. The reinforcement learning mayrefer to a learning method in which an agent defined in a certainenvironment learns to select a behavior or a behavior sequence thatmaximizes cumulative compensation in each state.

Machine learning, which is implemented as a deep neural network (DNN)including a plurality of hidden layers among artificial neural networks,is also referred to as deep learning, and the deep learning is part ofmachine learning. In the following, machine learning is used to meandeep learning.

<Robot>

A robot may refer to a machine that automatically processes or operatesa given task by its own ability. In particular, a robot having afunction of recognizing an environment and performing aself-determination operation may be referred to as an intelligent robot.

Robots may be classified into industrial robots, medical robots, homerobots, military robots, and the like according to the use purpose orfield.

The robot includes a driving interface may include an actuator or amotor and may perform various physical operations such as moving a robotjoint. In addition, a movable robot may include a wheel, a brake, apropeller, and the like in a driving interface, and may travel on theground through the driving interface or fly in the air.

<Self-Driving>

Self-driving refers to a technique of driving for oneself, and aself-driving vehicle refers to a vehicle that travels without anoperation of a user or with a minimum operation of a user.

For example, the self-driving may include a technology for maintaining alane while driving, a technology for automatically adjusting a speed,such as adaptive cruise control, a technique for automatically travelingalong a predetermined route, and a technology for automatically settingand traveling a route when a destination is set.

The vehicle may include a vehicle having only an internal combustionengine, a hybrid vehicle having an internal combustion engine and anelectric motor together, and an electric vehicle having only an electricmotor, and may include not only an automobile but also a train, amotorcycle, and the like.

Here, the self-driving vehicle may be regarded as a robot having aself-driving function.

<eXtended Reality (XR)>

Extended reality is collectively referred to as virtual reality (VR),augmented reality (AR), and mixed reality (MR). The VR technologyprovides a real-world object and background only as a CG image, the ARtechnology provides a virtual CG image on a real object image, and theMR technology is a computer graphic technology that mixes and combinesvirtual objects into the real world.

The MR technology is similar to the AR technology in that the realobject and the virtual object are shown together. However, in the ARtechnology, the virtual object is used in the form that complements thereal object, whereas in the MR technology, the virtual object and thereal object are used in an equal manner.

The XR technology may be applied to a head-mount display (HMD), ahead-up display (HUD), a mobile phone, a tablet PC, a laptop, a desktop,a TV, a digital signage, and the like. A device to which the XRtechnology is applied may be referred to as an XR device.

FIG. 1 is a block diagram illustrating an AI apparatus 100 according toan embodiment of the present disclosure.

Hereinafter, the AI apparatus 100 may be referred to as a terminal.

The AI apparatus (or an AI device) 100 may be implemented by astationary device or a mobile device, such as a TV, a projector, amobile phone, a smartphone, a desktop computer, a notebook, a digitalbroadcasting terminal, a personal digital assistant (PDA), a portablemultimedia player (PMP), a navigation device, a tablet PC, a wearabledevice, a set-top box (STB), a DMB receiver, a radio, a washing machine,a refrigerator, a desktop computer, a digital signage, a robot, avehicle, and the like.

Referring to FIG. 1 , the AI apparatus 100 may include a communicationinterface 110, an input interface 120, a learning processor 130, asensing interface 140, an output interface 150, a memory 170, and aprocessor 180.

The communication interface 110 may transmit and receive data to andfrom external devices such as other 100 a to 100 e and the AI server 200by using wire/wireless communication technology. For example, thecommunication interface 110 may transmit and receive sensor information,a user input, a learning model, and a control signal to and fromexternal devices.

The communication technology used by the communication interface 110includes GSM (Global System for Mobile communication), CDMA (CodeDivision Multi Access), LTE (Long Term Evolution), 5G, WLAN (WirelessLAN), Wi-Fi (Wireless-Fidelity), Bluetooth™, RFID (Radio FrequencyIdentification), Infrared Data Association (IrDA), ZigBee, NFC (NearField Communication), and the like.

The input interface 120 may acquire various kinds of data.

Here, the input interface 120 may include a camera for inputting a videosignal, a microphone for receiving an audio signal, and a user inputinterface for receiving information from a user. The camera or themicrophone may be treated as a sensor, and the signal acquired from thecamera or the microphone may be referred to as sensing data or sensorinformation.

The input interface 120 may acquire a training data for model learningand an input data to be used when an output is acquired by usinglearning model. The input interface 120 may acquire raw input data.Here, the processor 180 or the learning processor 130 may extract aninput feature by preprocessing the input data.

The learning processor 130 may learn a model composed of an artificialneural network by using training data. The learned artificial neuralnetwork may be referred to as a learning model. The learning model maybe used to an infer result value for new input data rather than trainingdata, and the inferred value may be used as a basis for determination toperform a certain operation.

Here, the learning processor 130 may perform AI processing together withthe learning processor 240 of the AI server 200.

Here, the learning processor 130 may include a memory integrated orimplemented in the AI apparatus 100. Alternatively, the learningprocessor 130 may be implemented by using the memory 170, an externalmemory directly connected to the AI apparatus 100, or a memory held inan external device.

The sensing interface 140 may acquire at least one of internalinformation about the AI apparatus 100, ambient environment informationabout the AI apparatus 100, and user information by using varioussensors.

Examples of the sensors included in the sensing interface 140 mayinclude a proximity sensor, an illuminance sensor, an accelerationsensor, a magnetic sensor, a gyro sensor, an inertial sensor, an RGBsensor, an IR sensor, a fingerprint recognition sensor, an ultrasonicsensor, an optical sensor, a microphone, a lidar, and a radar.

The output interface 150 may generate an output related to a visualsense, an auditory sense, or a haptic sense.

Here, the output interface 150 may include a display interface foroutputting time information, a speaker for outputting auditoryinformation, and a haptic module for outputting haptic information.

The memory 170 may store data that supports various functions of the AIapparatus 100. For example, the memory 170 may store input data acquiredby the input interface 120, training data, a learning model, a learninghistory, and the like.

The processor 180 may determine at least one executable operation of theAI apparatus 100 based on information determined or generated by using adata analysis algorithm or a machine learning algorithm. The processor180 may control the components of the AI apparatus 100 to execute thedetermined operation.

To this end, the processor 180 may request, search, receive, or utilizedata of the learning processor 130 or the memory 170. The processor 180may control the components of the AI apparatus 100 to execute thepredicted operation or the operation determined to be desirable amongthe at least one executable operation.

When the connection of an external device is required to perform thedetermined operation, the processor 180 may generate a control signalfor controlling the external device and may transmit the generatedcontrol signal to the external device.

The processor 180 may acquire intention information for the user inputand may determine the user's requirements based on the acquiredintention information.

The processor 180 may acquire the intention information corresponding tothe user input by using at least one of a speech to text (STT) enginefor converting speech input into a text string or a natural languageprocessing (NLP) engine for acquiring intention information of a naturallanguage.

At least one of the STT engine or the NLP engine may be configured as anartificial neural network, at least part of which is learned accordingto the machine learning algorithm. At least one of the STT engine or theNLP engine may be learned by the learning processor 130, may be learnedby the learning processor 240 of the AI server 200, or may be learned bytheir distributed processing.

The processor 180 may collect history information including theoperation contents of the AI apparatus 100 or the user's feedback on theoperation and may store the collected history information in the memory170 or the learning processor 130 or transmit the collected historyinformation to the external device such as the AI server 200. Thecollected history information may be used to update the learning model.

The processor 180 may control at least part of the components of AIapparatus 100 so as to drive an application program stored in memory170. Furthermore, the processor 180 may operate two or more of thecomponents included in the AI apparatus 100 in combination so as todrive the application program.

FIG. 2 is a block diagram illustrating an AI server 200 according to anembodiment of the present disclosure.

Referring to FIG. 2 , the AI server 200 may refer to a device thatlearns an artificial neural network by using a machine learningalgorithm or uses a learned artificial neural network. The AI server 200may include a plurality of servers to perform distributed processing, ormay be defined as a 5G network. Here, the AI server 200 may be includedas a partial configuration of the AI apparatus 100, and may perform atleast part of the AI processing together.

The AI server 200 may include a communication interface 210, a memory230, a learning processor 240, a processor 260, and the like.

The communication interface 210 can transmit and receive data to andfrom an external device such as the AI apparatus 100.

The memory 230 may include a model storage interface 231. The modelstorage interface 231 may store a learning or learned model (or anartificial neural network 231 a) through the learning processor 240.

The learning processor 240 may learn the artificial neural network 231 aby using the training data. The learning model may be used in a state ofbeing mounted on the AI server 200 of the artificial neural network, ormay be used in a state of being mounted on an external device such asthe AI apparatus 100.

The learning model may be implemented in hardware, software, or acombination of hardware and software. If all or part of the learningmodels are implemented in software, one or more instructions thatconstitute the learning model may be stored in memory 230.

The processor 260 may infer the result value for new input data by usingthe learning model and may generate a response or a control commandbased on the inferred result value.

FIG. 3 is a view illustrating an AI system 1 according to an embodimentof the present disclosure.

Referring to FIG. 3 , in the AI system 1, at least one of an AI server200, a robot 100 a, a self-driving vehicle 100 b, an XR device 100 c, asmartphone 100 d, or a home appliance 100 e is connected to a cloudnetwork 10. The robot 100 a, the self-driving vehicle 100 b, the XRdevice 100 c, the smartphone 100 d, or the home appliance 100 e, towhich the AI technology is applied, may be referred to as AI apparatuses100 a to 100 e.

The cloud network 10 may refer to a network that forms part of a cloudcomputing infrastructure or exists in a cloud computing infrastructure.The cloud network 10 may be configured by using a 3G network, a 4G orLTE network, or a 5G network.

That is, the devices 100 a to 100 e and 200 configuring the AI system 1may be connected to each other through the cloud network 10. Inparticular, each of the devices 100 a to 100 e and 200 may communicatewith each other through a base station, but may directly communicatewith each other without using a base station.

The AI server 200 may include a server that performs AI processing and aserver that performs operations on big data.

The AI server 200 may be connected to at least one of the AI apparatusesconstituting the AI system 1, that is, the robot 100 a, the self-drivingvehicle 100 b, the XR device 100 c, the smartphone 100 d, or the homeappliance 100 e through the cloud network 10, and may assist at leastpart of AI processing of the connected AI apparatuses 100 a to 100 e.

Here, the AI server 200 may learn the artificial neural networkaccording to the machine learning algorithm instead of the AIapparatuses 100 a to 100 e, and may directly store the learning model ortransmit the learning model to the AI apparatuses 100 a to 100 e.

Here, the AI server 200 may receive input data from the AI apparatuses100 a to 100 e, may infer the result value for the received input databy using the learning model, may generate a response or a controlcommand based on the inferred result value, and may transmit theresponse or the control command to the AI apparatuses 100 a to 100 e.

Alternatively, the AI apparatuses 100 a to 100 e may infer the resultvalue for the input data by directly using the learning model, and maygenerate the response or the control command based on the inferenceresult.

Hereinafter, various embodiments of the AI apparatuses 100 a to 100 e towhich the above-described technology is applied will be described. TheAI apparatuses 100 a to 100 e illustrated in FIG. 3 may be regarded as aspecific embodiment of the AI apparatus 100 illustrated in FIG. 1 .

<AI+Robot>

The robot 100 a, to which the AI technology is applied, may beimplemented as a guide robot, a carrying robot, a cleaning robot, awearable robot, an entertainment robot, a pet robot, an unmanned flyingrobot, or the like.

The robot 100 a may include a robot control module for controlling theoperation, and the robot control module may refer to a software moduleor a chip implementing the software module by hardware.

The robot 100 a may acquire state information about the robot 100 a byusing sensor information acquired from various kinds of sensors, maydetect (recognize) surrounding environment and objects, may generate mapdata, may determine the route and the travel plan, may determine theresponse to user interaction, or may determine the operation.

The robot 100 a may use the sensor information acquired from at leastone sensor among the lidar, the radar, and the camera so as to determinethe travel route and the travel plan.

The robot 100 a may perform the above-described operations by using thelearning model composed of at least one artificial neural network. Forexample, the robot 100 a may recognize the surrounding environment andthe objects by using the learning model, and may determine the operationby using the recognized surrounding information or object information.The learning model may be learned directly from the robot 100 a or maybe learned from an external device such as the AI server 200.

Here, the robot 100 a may perform the operation by generating the resultby directly using the learning model, but the sensor information may betransmitted to the external device such as the AI server 200 and thegenerated result may be received to perform the operation.

The robot 100 a may use at least one of the map data, the objectinformation detected from the sensor information, or the objectinformation acquired from the external device to determine the travelroute and the travel plan, and may control the driving interface suchthat the robot 100 a travels along the determined travel route andtravel plan.

The map data may include object identification information about variousobjects arranged in the space in which the robot 100 a moves. Forexample, the map data may include object identification informationabout fixed objects such as walls and doors and movable objects such aspollen and desks. The object identification information may include aname, a type, a distance, and a position.

In addition, the robot 100 a may perform the operation or travel bycontrolling the driving interface based on the control/interaction ofthe user. Here, the robot 100 a may acquire the intention information ofthe interaction due to the user's operation or speech utterance, and maydetermine the response based on the acquired intention information, andmay perform the operation.

<AI+Self-Driving>

The self-driving vehicle 100 b, to which the AI technology is applied,may be implemented as a mobile robot, a vehicle, an unmanned flyingvehicle, or the like.

The self-driving vehicle 100 b may include a self-driving control modulefor controlling a self-driving function, and the self-driving controlmodule may refer to a software module or a chip implementing thesoftware module by hardware. The self-driving control module may beincluded in the self-driving vehicle 100 b as a component thereof, butmay be implemented with separate hardware and connected to the outsideof the self-driving vehicle 100 b.

The self-driving vehicle 100 b may acquire state information about theself-driving vehicle 100 b by using sensor information acquired fromvarious kinds of sensors, may detect (recognize) surrounding environmentand objects, may generate map data, may determine the route and thetravel plan, or may determine the operation.

Like the robot 100 a, the self-driving vehicle 100 b may use the sensorinformation acquired from at least one sensor among the lidar, theradar, and the camera so as to determine the travel route and the travelplan.

In particular, the self-driving vehicle 100 b may recognize theenvironment or objects for an area covered by a field of view or an areaover a certain distance by receiving the sensor information fromexternal devices, or may receive directly recognized information fromthe external devices.

The self-driving vehicle 100 b may perform the above-describedoperations by using the learning model composed of at least oneartificial neural network. For example, the self-driving vehicle 100 bmay recognize the surrounding environment and the objects by using thelearning model, and may determine the traveling route by using therecognized surrounding information or object information. The learningmodel may be learned directly from the self-driving vehicle 100 a or maybe learned from an external device such as the AI server 200.

Here, the self-driving vehicle 100 b may perform the operation bygenerating the result by directly using the learning model, but thesensor information may be transmitted to the external device such as theAI server 200 and the generated result may be received to perform theoperation.

The self-driving vehicle 100 b may use at least one of the map data, theobject information detected from the sensor information, or the objectinformation acquired from the external device to determine the travelroute and the travel plan, and may control the driving interface suchthat the self-driving vehicle 100 b travels along the determined travelroute and travel plan.

The map data may include object identification information about variousobjects arranged in the space (for example, road) in which theself-driving vehicle 100 b travels. For example, the map data mayinclude object identification information about fixed objects such asstreet lamps, rocks, and buildings and movable objects such as vehiclesand pedestrians. The object identification information may include aname, a type, a distance, and a position.

In addition, the self-driving vehicle 100 b may perform the operation ortravel by controlling the driving interface based on thecontrol/interaction of the user. Here, the self-driving vehicle 100 bmay acquire the intention information of the interaction due to theuser's operation or speech utterance, and may determine the responsebased on the acquired intention information, and may perform theoperation.

<AI+XR>

The XR device 100 c, to which the AI technology is applied, may beimplemented by a head-mount display (HMD), a head-up display (HUD)provided in the vehicle, a television, a mobile phone, a smartphone, acomputer, a wearable device, a home appliance, a digital signage, avehicle, a fixed robot, a mobile robot, or the like.

The XR device 100 c may analyzes three-dimensional point cloud data orimage data acquired from various sensors or the external devices,generate position data and attribute data for the three-dimensionalpoints, acquire information about the surrounding space or the realobject, and render to output the XR object to be output. For example,the XR device 100 c may output an XR object including the additionalinformation about the recognized object in correspondence to therecognized object.

The XR device 100 c may perform the above-described operations by usingthe learning model composed of at least one artificial neural network.For example, the XR device 100 c may recognize the real object from thethree-dimensional point cloud data or the image data by using thelearning model, and may provide information corresponding to therecognized real object. The learning model may be directly learned fromthe XR device 100 c, or may be learned from the external device such asthe AI server 200.

Here, the XR device 100 c may perform the operation by generating theresult by directly using the learning model, but the sensor informationmay be transmitted to the external device such as the AI server 200 andthe generated result may be received to perform the operation.

<AI+Robot+Self-Driving>

The robot 100 a, to which the AI technology and the self-drivingtechnology are applied, may be implemented as a guide robot, a carryingrobot, a cleaning robot, a wearable robot, an entertainment robot, a petrobot, an unmanned flying robot, or the like.

The robot 100 a, to which the AI technology and the self-drivingtechnology are applied, may refer to the robot itself having theself-driving function or the robot 100 a interacting with theself-driving vehicle 100 b.

The robot 100 a having the self-driving function may collectively referto a device that moves for itself along the given route without theuser's control or moves for itself by determining the route by itself.

The robot 100 a and the self-driving vehicle 100 b having theself-driving function may use a common sensing method so as to determineat least one of the travel route or the travel plan. For example, therobot 100 a and the self-driving vehicle 100 b having the self-drivingfunction may determine at least one of the travel route or the travelplan by using the information sensed through the lidar, the radar, andthe camera.

The robot 100 a that interacts with the self-driving vehicle 100 bexists separately from the self-driving vehicle 100 b and may performoperations interworking with the self-driving function of theself-driving vehicle 100 b or interworking with the user who rides onthe self-driving vehicle 100 b.

Here, the robot 100 a interacting with the self-driving vehicle 100 bmay control or assist the self-driving function of the self-drivingvehicle 100 b by acquiring sensor information on behalf of theself-driving vehicle 100 b and providing the sensor information to theself-driving vehicle 100 b, or by acquiring sensor information,generating environment information or object information, and providingthe information to the self-driving vehicle 100 b.

Alternatively, the robot 100 a interacting with the self-driving vehicle100 b may monitor the user boarding the self-driving vehicle 100 b, ormay control the function of the self-driving vehicle 100 b through theinteraction with the user. For example, when it is determined that thedriver is in a drowsy state, the robot 100 a may activate theself-driving function of the self-driving vehicle 100 b or assist thecontrol of the driving interface of the self-driving vehicle 100 b. Thefunction of the self-driving vehicle 100 b controlled by the robot 100 amay include not only the self-driving function but also the functionprovided by the navigation system or the audio system provided in theself-driving vehicle 100 b.

Alternatively, the robot 100 a that interacts with the self-drivingvehicle 100 b may provide information or assist the function to theself-driving vehicle 100 b outside the self-driving vehicle 100 b. Forexample, the robot 100 a may provide traffic information includingsignal information and the like, such as a smart signal, to theself-driving vehicle 100 b, and automatically connect an electriccharger to a charging port by interacting with the self-driving vehicle100 b like an automatic electric charger of an electric vehicle.

<AI+Robot+XR>

The robot 100 a, to which the AI technology and the XR technology areapplied, may be implemented as a guide robot, a carrying robot, acleaning robot, a wearable robot, an entertainment robot, a pet robot,an unmanned flying robot, a drone, or the like.

The robot 100 a, to which the XR technology is applied, may refer to arobot that is subjected to control/interaction in an XR image. In thiscase, the robot 100 a may be separated from the XR device 100 c andinterwork with each other.

When the robot 100 a, which is subjected to control/interaction in theXR image, may acquire the sensor information from the sensors includingthe camera, the robot 100 a or the XR device 100 c may generate the XRimage based on the sensor information, and the XR device 100 c mayoutput the generated XR image. The robot 100 a may operate based on thecontrol signal input through the XR device 100 c or the user'sinteraction.

For example, the user can confirm the XR image corresponding to the timepoint of the robot 100 a interworking remotely through the externaldevice such as the XR device 100 c, adjust the self-driving travel pathof the robot 100 a through interaction, control the operation ordriving, or confirm the information about the surrounding object.

<AI+Self-Driving+XR>

The self-driving vehicle 100 b, to which the AI technology and the XRtechnology are applied, may be implemented as a mobile robot, a vehicle,an unmanned flying vehicle, or the like.

The self-driving driving vehicle 100 b, to which the XR technology isapplied, may refer to a self-driving vehicle having a means forproviding an XR image or a self-driving vehicle that is subjected tocontrol/interaction in an XR image. Particularly, the self-drivingvehicle 100 b that is subjected to control/interaction in the XR imagemay be distinguished from the XR device 100 c and interwork with eachother.

The self-driving vehicle 100 b having the means for providing the XRimage may acquire the sensor information from the sensors including thecamera and output the generated XR image based on the acquired sensorinformation. For example, the self-driving vehicle 100 b may include anHUD to output an XR image, thereby providing a passenger with a realobject or an XR object corresponding to an object in the screen.

Here, when the XR object is output to the HUD, at least part of the XRobject may be outputted so as to overlap the actual object to which thepassenger's gaze is directed. Meanwhile, when the XR object is output tothe display provided in the self-driving vehicle 100 b, at least part ofthe XR object may be output so as to overlap the object in the screen.For example, the self-driving vehicle 100 b may output XR objectscorresponding to objects such as a lane, another vehicle, a trafficlight, a traffic sign, a two-wheeled vehicle, a pedestrian, a building,and the like.

When the self-driving vehicle 100 b, which is subjected tocontrol/interaction in the XR image, may acquire the sensor informationfrom the sensors including the camera, the self-driving vehicle 100 b orthe XR device 100 c may generate the XR image based on the sensorinformation, and the XR device 100 c may output the generated XR image.The self-driving vehicle 100 b may operate based on the control signalinput through the external device such as the XR device 100 c or theuser's interaction.

FIG. 4 is a block diagram illustrating an AI apparatus 100 according toan embodiment of the present disclosure.

The redundant repeat of FIG. 1 will be omitted below.

In the present disclosure, the AI apparatus 100 may include an edgedevice.

The communication interface 110 may also be referred to as acommunicator.

Referring to FIG. 4 , the input interface 120 may include a camera 121for image signal input, a microphone 122 for receiving audio signalinput, and a user input interface 123 for receiving information from auser.

Voice data or image data collected by the input interface 120 areanalyzed and processed as a user's control command.

Then, the input interface 120 is used for inputting image information(or signal), audio information (or signal), data, or informationinputted from a user and the AI apparatus 100 may include at least onecamera 121 in order for inputting image information.

The camera 121 processes image frames such as a still image or a videoobtained by an image sensor in a video call mode or a capturing mode.The processed image frame may be displayed on the display interface 151or stored in the memory 170.

The microphone 122 processes external sound signals as electrical voicedata. The processed voice data may be utilized variously according to afunction (or an application program being executed) being performed inthe AI apparatus 100. Moreover, various noise canceling algorithms forremoving noise occurring during the reception of external sound signalsmay be implemented in the microphone 122.

The user input interface 123 is to receive information from a user andwhen information is inputted through the user input interface 123, theprocessor 180 may control an operation of the AI apparatus 100 tocorrespond to the inputted information.

The user input interface 123 may include a mechanical input means (or amechanical key, for example, a button, a dome switch, a jog wheel, and ajog switch at the front, back or side of the AI apparatus 100) and atouch type input means. As one example, a touch type input means mayinclude a virtual key, a soft key, or a visual key, which is displayedon a touch screen through software processing or may include a touch keydisposed at a portion other than the touch screen.

The sensing interface 140 may also be referred to as a sensor interface.

The output interface 150 may include at least one of a display interface151, a sound output module 152, a haptic module 153, or an opticaloutput module 154.

The display interface 151 may display (output) information processed inthe AI apparatus 100. For example, the display interface 151 may displayexecution screen information of an application program running on the AIapparatus 100 or user interface (UI) and graphic user interface (GUI)information according to such execution screen information.

The display interface 151 may be formed with a mutual layer structurewith a touch sensor or formed integrally, so that a touch screen may beimplemented. Such a touch screen may serve as the user input interface123 providing an input interface between the AI apparatus 100 and auser, and an output interface between the AI apparatus 100 and a user atthe same time.

The sound output module 152 may output audio data received from thewireless communication interface 110 or stored in the memory 170 in acall signal reception or call mode, a recording mode, a voicerecognition mode, or a broadcast reception mode.

The sound output module 152 may include a receiver, a speaker, and abuzzer.

The haptic module 153 generates various haptic effects that a user canfeel. A representative example of a haptic effect that the haptic module153 generates is vibration.

The optical output module 154 outputs a signal for notifying eventoccurrence by using light of a light source of the AI apparatus 100. Anexample of an event occurring in the AI apparatus 100 includes messagereception, call signal reception, missed calls, alarm, schedulenotification, e-mail reception, and information reception through anapplication.

FIG. 5 is a flowchart illustrating an embodiment of the presentdisclosure.

Fundamentally, the artificial intelligence apparatus 100 of the presentdisclosure may be mounted in an apparatus requiring speech recognitionto receive an utterance of a user, and control a speech recognitionapparatus such that the apparatus requiring speech recognition providesa speech recognition service via recognition and analysis.

Referring to FIG. 5 , the input interface 120 of the artificialintelligence apparatus 100 may receive speech data of a user (S510). Atthis time, the speech data may include data spoken by the user andreceived by the input interface 120 of the artificial intelligenceapparatus 100 or data received from an external device via thecommunication interface 110 of the artificial intelligence apparatus100.

Specifically, the speech data of the user may include at least oneutterance, at least one non-utterance interval and a wakeup word.

At this time, the wakeup word may mean a specific command for activatinga speech recognition function. The speech recognition function may beactivated when the utterance of the user includes the wakeup word, andthe speech recognition function may be deactivated when the utterancedoes not include the wakeup word, without being limited thereto.

In addition, activation of the speech recognition function may includeactivation of a microphone included in the input interface, anddeactivation of the speech recognition function may include deactivationof the microphone included in the input interface.

According to the present disclosure, the at least one utterance mayinclude speech data including a speech command to be processed by theprocessor 180. In addition, the at least one non-utterance interval mayinclude an interval present between speech commands to be processed bythe processor 180. For example, the speech data may include a wakeupword, a first utterance, a second utterance and a non-utterance intervalbetween the first utterance and the second utterance.

According to the present disclosure, the processor 180 may detect thenon-utterance interval included in the speech data (S520). At this time,the non-utterance interval may include an interval in which there is nospeech data input via the input interface 120 or the amplitude of thereceived speech data is less than a specific value.

The processor 180 may detect the non-utterance interval of the receivedspeech data (S520), and extract an utterance before the non-utteranceinterval as a first utterance. In addition, the processor 180 maydetermine presence/absence of the second utterance based on thecharacteristics of the extracted first utterance (S530). The processS530 will be described in detail with reference to FIG. 6 .

According to the present disclosure, the processor 180 may maintain theactivated speech recognition upon determining that the second utteranceis present based on the characteristics of the first utterance, andperform a first speech command according to the first utterance beforethe non-utterance interval while the second utterance after thenon-utterance interval is received as the activated speech recognitionis maintained (S540). In addition, when the second utterance isreceived, the processor 180 may perform a second speech commandaccording to the second utterance (S550).

According to the present disclosure, the processor 180 may stop theactivated speech recognition upon determining that the second utteranceis not present based on the characteristics of the first utterance, andperform the first speech command according to the first utterance beforethe non-utterance interval (S551).

For example, assume that the speech data received by the input interface120 of the artificial intelligence apparatus 100 is “HI LG! Turn on theTV and um, . . . Mute the TV”. At this time, “HI LG!” may be a wakeupword for activating speech recognition of the artificial intelligenceapparatus 100. The processor 180 may activate the speech recognitionfunction of the artificial intelligence apparatus 100 when “HI LG” isreceived.

When the speech recognition function is activated, the processor 180 maydetect the non-utterance interval. When “Turn on the TV and um, . . . ”is received, the processor 180 may detect an interval “ . . . ” in whichthe amplitude of the received speech data is less than a specific value.When “ . . . ” which is the detected non-utterance interval exceeds aset time, the processor 180 may extract “Turn on the TV and um”, whichis the speech data before the non-utterance interval, as the firstutterance.

The processor 180 may determine presence/absence of an additionalutterance after the non-utterance interval according to thecharacteristics of “Turn on the TV and um” which is the first utterancebefore the non-utterance interval.

The processor 180 may maintain the activated speech recognition upondetermining that the second utterance is present according to thecharacteristics of “Turn on the TV and um”. The processor 180 mayreceive “mute the TV”, which is the second utterance after thenon-utterance interval, as the activated speech recognition ismaintained.

In addition, the processor 180 may perform the first speech commandaccording to the first utterance before the non-utterance interval while“mute the TV” which is the second utterance is received (S540). That is,the processor 180 may control the TV to perform operation of turning on“the power of the TV” while “mute the TV” is received. In addition, whenthe second utterance is received, the processor 180 may perform thesecond speech command according to the second utterance (S550). That is,the processor 180 may control the TV to mute the TV.

Hereinafter, the example of the case where the processor determines thatthe second utterance is not present will be described.

For example, assume that there is speech data “HI LG! Turn on the TV”.

At this time, HI LG! may be a wakeup word for activating speechrecognition of the artificial intelligence apparatus 100. When “HI LG”is received, the processor 180 may activate the speech recognitionfunction of the artificial intelligence apparatus 100.

When the speech recognition function is activated, the processor 180 maydetect the non-utterance interval. When “Turn on the TV” is received,the processor 180 may detect an interval in which the amplitude of thereceived speech data is less than the specific value. When the detectednon-utterance interval exceeds the set time, the processor 180 mayextract “Turn on the TV”, which is the speech data before thenon-utterance interval, as the first utterance.

The processor 180 may determine whether an additional utterance ispresent after the non-utterance interval according to thecharacteristics of “Turn on the TV” which is the first utterance beforethe non-utterance interval.

The processor 180 may stop the activated speech recognition upondetermining that the second utterance is not present according to thecharacteristics of “Turn on the TV”. As the activated speech recognitionis stopped, the processor 180 may no longer receive the speech data.Thereafter, the processor 180 may perform the first speech commandaccording to “Turn on the TV” which is the first utterance before thenon-utterance interval (S551). That is, the processor 180 may controlthe TV to turn on the TV.

FIG. 6 is a flowchart illustrating an embodiment of the presentdisclosure.

Referring to FIG. 6 , the processor 180 may extract the utterance beforethe non-utterance interval as the first utterance (S610), after thenon-utterance interval is detected (S520). Specifically, the processor180 may extract the speech data immediately before the non-utteranceinterval as the first utterance.

In addition, the processor 180 may input, to an artificial intelligencemodel, the first utterance extracted to determine presence/absence ofthe second utterance (S620). Specifically, the processor 180 may inputthe characteristic data of the first utterance to the artificialintelligence model as an input value 710.

At this time, the characteristic data of the first utterance is acharacteristic value representing components useful for speechrecognition and may be extracted from the first utterance.

In addition, the characteristic data of the first utterance may includecharacteristics extracted by applying LPC cepstrum, PLP cepstrum, Melfrequency cepstral coefficient (MFCC), filter band energy used forspeech recognition to the first utterance. In addition, thecharacteristic data of the first utterance input to the artificialintelligence model may include sequence data including speechcharacteristics and context characteristics of the preprocessed firstutterance.

The processor 180 may input the first utterance to the artificialintelligence model and determine presence/absence of the secondutterance after the non-utterance interval as a result value (S630).Specifically, the artificial intelligence model may include aclassification model for extracting the speech characteristics and thecontext characteristics of the first utterance and determiningpresence/absence of the second utterance, when the first utterance isinput. Hereinafter, the artificial intelligence model will be describedwith reference to FIG. 7 .

According to the present disclosure, when the result value 730 of theartificial intelligence model is “the second utterance is present (1)”,the processor 180 may maintain the activated speech recognition (S640).The processor 180 may perform the first speech command according to thefirst utterance while the second utterance is received, when theactivated speech recognition is maintained (S540). In addition, theprocessor 180 may perform the second speech command according to thesecond utterance when the second utterance is received (S550).

According to the present disclosure, when the result value 730 of theartificial intelligence model is “the second utterance is not present(0)”, the processor 180 may deactivate the speech recognition function(S641). In addition, the processor may perform the first speech commandaccording to the first utterance without receiving the second utterance(S551).

FIG. 7 is a view illustrating an artificial intelligence model accordingto an embodiment of the present disclosure.

Referring to FIG. 7 , the processor 180 may determine presence/absenceof the second utterance after the non-utterance interval according tothe characteristics of the first utterance using the artificialintelligence model.

At this time, the artificial intelligence model may include a classifierused for speech recognition. The artificial intelligence model mayinclude logistic regression, SVM, decision tree, random forest, neuralnetworks, etc. as a classifier.

According to the present disclosure, the artificial intelligence modelmay use various known algorithms and may include a recurrent neuralnetwork (RNN) model. At this time, the RNN may be an artificialintelligence model suitable for learning variable data such as sequencedata. The RNN may include a hidden state. At this time, the hidden stateis information including the characteristics of previous input data,and, when new input data is input, the RNN may output a result valuereflecting information on the entire sequence data by applying aprevious hidden state. In addition, according to the present disclosure,the artificial intelligence model may be an LSTM model which is animproved RNN model.

According to the present disclosure, the processor 180 may input thecharacteristic data of the first utterance as an input value 710 of theartificial intelligence model. At this time, the characteristic data ofthe first utterance may include sequence data including the speechcharacteristics and context characteristics of the preprocessed firstutterance.

At this time, “the sequence data being input to the artificialintelligence model” may mean that the characteristic data such as words,articles, connective words or connective endings included in the firstutterance is sequentially input to the artificial intelligence model inorder of time from X₁ to X_(t), as shown in FIG. 7 .

According to the present disclosure, when the characteristic data of thefirst utterance X is input to the artificial intelligence model at atime X_(t), the hidden state 720 may include a state in which the entiresequence characteristic information of the first utterance reflectinginformation including the previous characteristics of X at time from X₁to X_(t-1) of the first utterance is stored. The artificial intelligencemodel may output a result value by applying the characteristic dataincluded in the hidden state 720.

At this time, the result value 730 may include a classification modelfor determining presence/absence of the second utterance. Specifically,the artificial intelligence model may output “the second utterance ispresent (1)” or “the second utterance is not present (0)” as the resultvalue 730 according to the characteristics of the first utterance.

Hereinafter, the characteristic data included in the first utterancewill be described.

According to the present disclosure, the processor 180 may input thefirst utterance to the artificial intelligence model, and the artificialintelligence model may include a classification model for extracting thespeech characteristics and the context characteristics of the firstutterance when the first utterance is received and classifyingpresence/absence of the second utterance.

At this time, the process of extracting the speech characteristics andthe context characteristics of the first utterance may include apreprocessing procedure performed in the artificial intelligenceapparatus or an external device.

Specifically, the artificial intelligence model may include an LS™ modeltrained by extracting the speech characteristics and the contextcharacteristics based on the first utterance. At this time, the speechcharacteristics may include frame-unit characteristics in the firstutterance such as pitch trends including the pitch of the speech data,intensity indicating the intensity of the speech data, general speechcharacteristics such as spectral stability.

In addition, the context characteristics may include word embeddingbased on an ASR result included in the first utterance.

With respect to the speech characteristics, the artificial intelligencemodel may include a model trained to determine that the second utteranceis present when the pitch of the first utterance is constantlymaintained for a predetermined time or more or when change in the pitchof the first utterance by a predetermined value or more is detected.

For example, the artificial intelligence model assumes that the firstutterance is “Turn on the TV. Um”. The processor 180 may include a modeltrained to extract “Turn on the TV. Um” as the first utterance, anddetermine that the second utterance is present 1) when the pitch of “Um”in “Turn on the TV. Um” is constantly maintained for a predeterminedtime or more or 2) when change between the pitches of “Turn on the TV”and “Um” in “Turn on the TV. Um” by the predetermined value or more isdetected, in the speech characteristics of the extracted firstutterance.

This is designed in view of the characteristics indicating that thepitch of the utterance is constantly maintained for a predetermined time(“Um . . . ”) and the pitch of the utterance is different (in “Turn onthe TV” and “Um”, the pitch of “Um” is generally lower), inconsideration of the pitch of the utterance including the speech command(“Turn on the TV”) and the speech received when the user stutters orthinks (“Um . . . ”).

In addition, with respect to the context characteristics, the artificialintelligence model may include a model trained to determine that thesecond utterance is present when characteristics related to a connectiveword or a connective ending are present in the context characteristicsof the first utterance.

For example, assume that the first utterance is “Turn on the TV and”.The artificial intelligence model may include a model trained to extract“Turn on the TV and” as the first utterance and determine that thesecond utterance is present when the characteristics related to theconnective word or the connective ending are present in the contextcharacteristics of the extracted first utterance.

That is, when the characteristics related to the connective word or theconnective ending, such as “and”, “next”, “then” or “after”, are presentin the ending of the first utterance, the artificial intelligence modelmay be trained to determine that the second utterance is present. Thisis designed in consideration of the characteristics that the connectiveword “and” is included in the ending of the utterance “Turn on the TVand” including the speech command.

Meanwhile, in the present disclosure, determination as topresence/absence of the second utterance using the speechcharacteristics or the context characteristics is not limited to theabove-described speech characteristics or context characteristics.

Hereinafter, the speech data received by the input interface will bedescribed with reference to FIG. 8 .

FIG. 8 is a view illustrating an example of utterance according to anembodiment of the present disclosure.

According to the present disclosure, the input interface 12 of theartificial intelligence apparatus may receive speech data spoken by auser. At this time, the speech data may include a waveform including amixture of a speech and noise.

FIG. 8 shows the waveform of the speech data spoken by the user. Thespeech data spoken by the user may include a first utterance, anon-utterance interval and a second utterance.

For example, when the speech data “Turn on the TV and . . . mute theTV”, “Turn on the TV and” may correspond to the first utterance, “ . . .” may correspond to the non-utterance interval, and “mute the TV” maycorrespond to the second utterance.

The processor 180 may analyze the received speech data to detect thenon-utterance interval 820, and extract the speech data after thenon-utterance interval 820 as the second utterance upon determining thatthe second utterance is present according to the characteristics of thefirst utterance before the non-utterance interval.

Hereinafter, the scenario of the present disclosure will be described.

FIG. 9 is a view illustrating a scenario according to an embodiment ofthe present disclosure.

According to the present disclosure, the artificial intelligenceapparatus 100 may further include an output interface 150 responding toa speech command, and the processor 180 may control the output interface150 to output a notification indicating that the second utterance ispossible while the first speech command is performed.

Specifically, the processor 180 may perform the first speech commandaccording to the first utterance before the non-utterance interval whilethe second utterance is received. At this time, as the method ofperforming the first speech command, the processor 180 may control theoutput interface 150 to perform the output corresponding to the firstspeech command.

In addition, the processor 180 may output a notification indicating thatthe second utterance is currently possible while the first speechcommand is performed.

Hereinafter, the detailed scenario will be described.

In the present disclosure, the user may give a speech command to theartificial intelligence apparatus using speech recognition. For example,assume that the user wants to obtain weather information. The userspeaks “Hi LG! I want to know today's weather and . . . ”.

The input interface 120 of the artificial intelligence apparatus 100 mayreceive the speech data according to the utterance of the user “Hi LG! Iwant to know today's weather and Um . . . ”. The processor 180 of theartificial intelligence apparatus 100 may recognize the wakeup word “HiLG!” and activate the speech recognition function. In addition, thenon-utterance interval “ . . . ” included in the speech data received bythe input interface may be detected.

When the non-utterance interval “ . . . ” exceeds the set time, theprocessor 180 may extract “I want to know today's weather and” beforethe non-utterance interval. The processor 180 may input “I want to knowtoday's weather and” to the artificial intelligence model using thefirst utterance. It is possible to determine whether the secondutterance is present via the context characteristics or the speechcharacteristics of the first utterance.

Specifically, the artificial intelligence model may determine that thesecond utterance is present, when the pitch of the ending “and” of “wantto know today's weather and” of the first utterance is constantlymaintained for a predetermined time or more, and maintain the activatedspeech recognition.

In addition, the artificial intelligence model may determine that thesecond utterance is present, when change between the pitches of theending “Um” and “want to know today's weather and” of “want to knowtoday's weather and Um” of the first utterance is equal to or greaterthan the predetermined value, and maintain the activated speechrecognition.

In addition, the artificial intelligence model may determine that thesecond utterance is present, when there are characteristics related tothe connective word or the connective ending such as “and”, inconsideration of the context characteristics of “want to know today'sweather and” of the first utterance, and maintain the activated speechrecognition.

Meanwhile, in the present disclosure, the configuration for determiningwhether the second utterance is present according to the speechcharacteristics or context characteristics of the first utterance is notlimited to the above example.

Upon determining that the result value of the artificial intelligencemodel according to the characteristics of the first utterance is “thesecond utterance is present (1), the processor 180 may receive thesecond utterance after the non-utterance interval “Please check today'sschedule on the calendar” as the activated speech recognition ismaintained. In addition, while the second utterance is received, thefirst speech command according to the first utterance “I want to knowtoday's weather and” may be performed. In addition, when the secondutterance “Please check today's schedule on the calendar” is received,the second speech command according to the second utterance may beperformed.

According to the present disclosure, the processor 180 may control theoutput interface 150 to output a notification indicating that the firstspeech command according to reception of the first utterance isperformed. At this time, the output interface 150 may be mounted in theartificial intelligence apparatus or may be mounted in the externaldevice to be controlled via the communication interface 110.

Referring to FIG. 9 , the processor 180 may control the output interface150 to perform the first speech command corresponding to the firstutterance “I want to know today's weather and”. Specifically, an example921 of the output of the weather is shown in FIG. 9 .

In addition, the processor 180 may control the output interface 150 tooutput a notification 922 indicating that the second utterance ispossible while the first speech command is performed (921). For example,the processor 180 may include a method of interacting with the user,such as a method of controlling the lighting of a specific part of theartificial intelligence apparatus (for example, blinking of the eyes ofthe artificial intelligence apparatus or a method of displaying aspecific characteristic or symbol indicating that the speech recognitionfunction is activated), as the notification indicating that the secondutterance is possible.

Referring to FIG. 9 , the processor 180 may receive the speech datacorresponding to the second utterance “check today's schedule on thecalendar” while the output interface 150 is controlled to display theweather information corresponding to the first speech command.Thereafter, the processor 180 may perform operation of checking thetoday's schedule on the calendar corresponding to the second utterance.

In another example, assume that the processor 180 receives the speechdata “Please change the channel of the TV, . . . mute the TV”. Theprocessor 180 may extract “Turn on the TV” as the first utterance.

In addition, the processor 180 may detect the speech characteristics andcontext characteristics of “change the channel of the TV and” using theartificial intelligence model and determine that the second utterance“mute the TV” is present. The processor 180 may perform a commandcorresponding to the first utterance “Please change the change of theTV” while the second utterance “mute the TV” is received.

At this time, the processor 180 may perform control such that the TVdoes not transmit a sound source, in order to receive the secondutterance of the user. In addition, when a channel change commandaccording to the first utterance is performed, the second utterance maybe received while the channel is changed without a response (TTS) forinteraction with the user. In addition, the processor 180 may controlthe output interface 150 of the TV and display a notification indicatingthat the second utterance is possible.

A method of operating an artificial intelligence apparatus may includereceiving speech data, detecting a non-utterance interval included inthe speech data, and determining presence/absence of a second utteranceafter the non-utterance interval according to characteristics of a firstutterance before the non-utterance interval, when the non-utteranceinterval exceeds a set time.

The method may further include receiving a wakeup word to activatespeech recognition before the first utterance is received andmaintaining the activated speech recognition, upon determining that thesecond utterance is present.

The method may further include performing a first speech commandaccording to the first utterance before the non-utterance interval whilethe second utterance after the non-utterance interval is received as theactivated speech recognition is maintained and performing a secondspeech command according to the second utterance when the secondutterance is received.

The determining of the presence/absence of the second utterance afterthe non-utterance interval may include acquiring presence/absence of thesecond utterance output by an artificial intelligence model, byinputting the first utterance to the artificial intelligence model, andthe artificial intelligence model may include a classification model forextracting speech characteristics and context characteristics of thefirst utterance and determining presence/absence of the secondutterance, when the first utterance is input.

The artificial intelligence model may be trained to determine that thesecond utterance is present, when a pitch of the first utterance isconstantly maintained for a predetermined time or more or when change inpitch of the first utterance by a predetermined value or more isdetected.

The artificial intelligence model may be trained to determine that thesecond utterance is present, when characteristics related to aconnective word or a connective ending are present in the contextcharacteristics of the first utterance.

The method may further include controlling an output interface to outputa notification indicating that the second utterance is possible whilethe first speech command is performed.

The method may further include detecting a second non-utterance intervalafter the second utterance, determining presence/absence of a thirdutterance after the second non-utterance interval according tocharacteristics of the second utterance before the second non-utteranceinterval, when the second non-utterance interval exceeds the set time,performing the second speech command according to the second utterancebefore the second non-utterance interval while the third utterance afterthe second non-utterance interval is received, and performing a thirdspeech command according to the third utterance when the third utteranceis received.

The non-utterance interval may be an interval in which an amplitude ofthe speech data is less than a specific value.

The method may further include receiving the second utterance while afirst speech command according to the first utterance is performed, upondetermining that the second utterance is present; and performing asecond speech command according to the second utterance when the secondutterance is received.

According to the present disclosure, by detecting a non-utteranceinterval between speech data and receiving a second utterance whileoperation for a first utterance before the non-utterance interval isperformed, a wakeup word for activating a speech recognition functionagain after a speech command for the first utterance is performed is notnecessary. Therefore, time delay does not occur when the secondutterance is received, and operation according to the additional secondutterance can be rapidly performed.

In addition, by using an artificial intelligence model for determiningwhether the second utterance of the user is present according to thecharacteristics of the first utterance, it is possible to clearlyunderstand the intention of a user.

Flowcharts according to the present disclosure may be performedregardless of the order or concurrently. That is, they are notconstrained in time-series order.

Other implementations are within the scope of the following claims.

The present disclosure can be made in software, firmware or acombination of software and firmware.

The present disclosure may include one or more processors. The one ormore processors may include ‘the processor 180’ or ‘a processor foroperating an artificial intelligence model’.

According to an embodiment of the present disclosure, theabove-described method may be implemented as a processor-readable codein a medium where a program is recorded. Examples of aprocessor-readable medium may include hard disk drive (HDD), solid statedrive (SSD), silicon disk drive (SDD), read-only memory (ROM), randomaccess memory (RAM), CD-ROM, a magnetic tape, a floppy disk, and anoptical data storage device.

What is claimed is:
 1. An artificial intelligence apparatus comprising:at least one sensor configured to obtain audio data; and one or moreprocessors configured to: obtain a wakeup word, via the at least onesensor, to activate speech recognition; based on activating the speechrecognition, detect a non-utterance interval in the audio data; andbased on a length of the non-utterance interval exceeding a thresholdlength: extract a first utterance preceding the non-utterance intervalin the audio data; determine whether a second utterance follows thenon-utterance interval based on one or more characteristics of the firstutterance by inputting the first utterance to an artificial intelligencemodel for extracting one or more speech characteristics and one or morecontext characteristics of the first utterance; and maintain theactivated speech recognition, upon determining that the second utterancefollows the non-utterance interval.
 2. The artificial intelligenceapparatus of claim 1, wherein the one or more processors are furtherconfigured to: perform a first speech command according to the firstutterance while the second utterance is obtained as the activated speechrecognition is maintained; and perform a second speech command accordingto the second utterance after the second utterance is obtained.
 3. Theartificial intelligence apparatus of claim 2, further comprising anoutput interface configured to respond to a speech command, wherein theone or more processors are further configured to, while the first speechcommand is performed, control the output interface to output anotification indicating that obtaining of the second utterance ispossible.
 4. The artificial intelligence apparatus of claim 3, whereinthe one or more processors are further configured to control the outputinterface to output the notification by controlling the output interfaceto display a visual characteristic indicating that the speechrecognition is activated.
 5. The artificial intelligence apparatus ofclaim 2, wherein the one or more processors are further configured to:detect a second non-utterance interval following the second utterance inthe audio data; and based on a length of the second non-utteranceinterval exceeding the threshold length: determine whether a thirdutterance follows the second non-utterance interval based on one or morecharacteristics of the second utterance; based on determining that thethird utterance follows the second non-utterance interval, perform thesecond speech command while the third utterance is obtained; and performa third speech command according to the third utterance after the thirdutterance is obtained.
 6. The artificial intelligence apparatus of claim1, wherein the artificial intelligence model is trained to determinethat the second utterance follows the non-utterance interval, based on apitch of the first utterance being maintained for at least apredetermined time or based on a change in the pitch of the firstutterance by at least a predetermined value being detected.
 7. Theartificial intelligence apparatus of claim 1, wherein the artificialintelligence model is trained to determine that the second utterancefollows the non-utterance interval, based on characteristics related toa connective word or a connective ending being present in the one ormore context characteristics of the first utterance.
 8. The artificialintelligence apparatus of claim 1, wherein the non-utterance interval isan interval in which an amplitude of the audio data is less than aspecific value.
 9. The artificial intelligence apparatus of claim 1,wherein the one or more processors are further configured to: obtain thesecond utterance while a first speech command according to the firstutterance is performed, upon determining that the second utterancefollows the non-utterance interval; and perform a second speech commandaccording to the second utterance after the second utterance isobtained.
 10. A method of operating an artificial intelligenceapparatus, the method comprising: obtaining audio data; obtaining awakeup word to activate speech recognition; based on activating thespeech recognition, detecting a non-utterance interval in the audiodata; and based on a length of the non-utterance interval exceeding athreshold length: extracting a first utterance preceding thenon-utterance interval in the audio data; determining whether a secondutterance follows the non-utterance interval based on one or morecharacteristics of the first utterance by inputting the first utteranceto an artificial intelligence model for extracting one or more speechcharacteristics and one or more context characteristics of the firstutterance; and maintaining the activated speech recognition, upondetermining that the second utterance follows the non-utteranceinterval.
 11. The method of claim 10, further comprising: performing afirst speech command according to the first utterance while the secondutterance is obtained as the activated speech recognition is maintained;and performing a second speech command according to the second utteranceafter the second utterance is obtained.
 12. The method of claim 11,further comprising: while the first speech command is performed,controlling an output interface to output a notification indicating thatobtaining of the second utterance is possible.
 13. The method of claim12, wherein controlling the output interface to output the notificationcomprises controlling the output interface to display a visualcharacteristic indicating that the speech recognition is activated. 14.The method of claim 11, further comprising: detecting a secondnon-utterance interval following the second utterance in the audio data;and based on a length of the second non-utterance interval exceeding thethreshold length: determining whether a third utterance follows thesecond non-utterance interval based on one or more characteristics ofthe second utterance; based on determining that the third utterancefollows the second non-utterance interval, performing the second speechcommand while the third utterance is obtained; and performing a thirdspeech command according to the third utterance after the thirdutterance is obtained.
 15. The method of claim 10, wherein theartificial intelligence model is trained to determine that the secondutterance follows the non-utterance interval, based on a pitch of thefirst utterance being maintained for at least a predetermined time orbased on a change in the pitch of the first utterance by at least apredetermined value being detected.
 16. The method of claim 10, whereinthe artificial intelligence model is trained to determine that thesecond utterance is follows the non-utterance interval, based oncharacteristics related to a connective word or a connective endingbeing present in the one or more context characteristics of the firstutterance.
 17. The method of claim 10, wherein the non-utteranceinterval is an interval in which an amplitude of the audio data is lessthan a specific value.
 18. The method of claim 10, further comprising:obtaining the second utterance while a first speech command according tothe first utterance is performed, upon determining that the secondutterance follows the non-utterance interval; and performing a secondspeech command according to the second utterance after the secondutterance is obtained.
 19. A non-transitory computer-readable mediumstoring instructions that, when executed by one or more processors,cause the one or more processors to: obtain audio data; obtain a wakeupword to activate speech recognition; based on activating the speechrecognition, detect a non-utterance interval in the audio data; andbased on a length of the non-utterance interval exceeding a thresholdlength: extract a first utterance preceding the non-utterance intervalin the audio data; determine whether a second utterance follows thenon-utterance interval based on one or more characteristics of the firstutterance by inputting the first utterance to an artificial intelligencemodel for extracting one or more speech characteristics and one or morecontext characteristics of the first utterance; and maintain theactivated speech recognition, upon determining that the second utterancefollows the non-utterance interval.